Compara mètodes

	Disseny pragmàtic de mètodes mixts	Disseny explicatiu seqüencial de mètodes mixts
Camp	Disseny de recerca	Disseny de recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	Early 2000s (formalised); pragmatism as philosophy late 19th–early 20th century	2007 (formalized in Creswell & Plano Clark's mixed methods typology)
Autor original	John W. Creswell & Vicki L. Plano Clark (formalised); philosophical grounding in William James, John Dewey, Richard Rorty	John W. Creswell & Vicki L. Plano Clark
Tipus	Mixed methods research design	Mixed methods research design
Font seminal	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications. ISBN: 978-1483344379	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
Àlies	pragmatic MMR, pragmatism-guided mixed methods, pragmatic inquiry design, practical mixed methods	explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design
Relacionats	6	6
Resum	Pragmatic mixed methods design is a research approach that selects and combines quantitative and qualitative methods based on what best answers the research question, rather than adhering to a single philosophical paradigm. Rooted in the philosophical tradition of pragmatism — associated with William James, John Dewey, and later Richard Rorty — it treats methodological fit and practical utility as the primary criteria for design decisions. The approach is endorsed by leading mixed methods scholars including Creswell and Plano Clark as the most common philosophical worldview underpinning mixed methods work.	The explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation.